Identifier

Author

Degree

Master of Science (MS)

Department

Biological Sciences

Document Type

Thesis

Abstract

Modern crocodylian systematics has been dominated by investigations of higher-level relationships aimed at resolving the disparity between morphological and molecular data, especially regarding the true gharial (Gavialis). Consequently, no studies to date have provided adequate resolution of the interspecific relationships within the most broadly distributed, ecologically diverse, and species-rich crocodylian genus, Crocodylus. In this study, Bayesian and ML partitioned phylogenetic analyses were performed on a DNA sequence dataset of 7,282 base pairs representing four mitochondrial regions, nine nuclear loci, and all 23 crocodylian species. The analyses were performed on a suite of partitioning strategies to investigate the modeling effects of partition choice in phylogenetic analyses. Bayesian lognormal relaxed-clock dating analyses also were performed on the dataset, calibrated from the rich crocodylian fossil record. A robust interspecific phylogeny of Crocodylus is reconstructed, and subsequently used in ML and Bayesian ancestral character-state reconstructions to test hypotheses about the biogeographic history and evolutionary ecology of the genus. The results demonstrate that the genus originated from an ancestor in the tropics of the Late Miocene Indo-Pacific, and rapidly radiated and dispersed around the globe during a period marked by mass extinctions of fellow crocodylians. The results also prove paraphyly of Crocodylus, and reveal more diversity within the genus than recognized by current taxonomy. This study also establishes a baseline for assessing the utility of various model selection criteria for objectively selecting the optimal partitioning strategy within ML and Bayesian frameworks. The results indicate that gene identity is a poor method of partition choice. Furthermore, the results of the ancestral character-state reconstructions suggest ML and Bayesian methods produce more realistic and reliable results than parsimony.